论文标题
具有机器人群的化学风格的模式形成
Chemistry-Inspired Pattern Formation with Robotic Swarms
论文作者
论文摘要
在产生复杂结构（例如化学元件和分子）的粒子相互作用中可以广泛看到自组织的新兴模式。受这些互动的启发，这项工作提出了一种新颖的随机方法，该方法使一群异质机器人以完全分散的方式创建新兴的模式，并且仅依靠本地信息。我们的方法包括将群构型建模为动态Gibbs随机场（GRF），并在受到化学规则启发的邻域系统上的设置约束，这些规则决定了粒子之间的结合极性。使用GRF模型，我们确定每个机器人的速度，从而导致导致图案或形状创建的行为。模拟实验显示了该方法在产生多种模式中的多功能性，并且使用一组物理机器人的实验显示了潜在应用中的可行性。
Self-organized emergent patterns can be widely seen in particle interactions producing complex structures such as chemical elements and molecules. Inspired by these interactions, this work presents a novel stochastic approach that allows a swarm of heterogeneous robots to create emergent patterns in a completely decentralized fashion and relying only on local information. Our approach consists of modeling the swarm configuration as a dynamic Gibbs Random Field (GRF) and setting constraints on the neighborhood system inspired by chemistry rules that dictate binding polarity between particles. Using the GRF model, we determine velocities for each robot, resulting in behaviors that lead to the creation of patterns or shapes. Simulated experiments show the versatility of the approach in producing a variety of patterns, and experiments with a group of physical robots show the feasibility in potential applications.
